The post-party cleanup challenge isn't always what's left on top of the furniture, it's what was hiding underneath.
SEATTLE, June 30, 2026 /PRNewswire/ -- A room can look spotless from the moment guests arrive to when they leave. The floors are vacuumed, surfaces are wiped down, pillows are fluffed, and everything appears clean and in its place until someone moves a chair or slides an ottoman across the floor. Suddenly, a different picture appears.
Crumbs beneath dining chairs. Dust along baseboards. Pet hair tucked underneath a couch. Dirt tracked into corners that rarely see daylight. Areas hidden during everyday life become impossible to ignore once furniture is temporarily rearranged.
It's a familiar experience that highlights an overlooked reality of modern cleaning habits: people tend to clean what they can easily see.
The challenge isn't necessarily that these hidden areas are forgotten. It's that reaching them often requires moving furniture, changing routines, and adding extra effort to an already time-consuming task. As a result, some of the dirtiest parts of a room can remain concealed beneath spaces that otherwise appear clean and well-maintained.
The Tineco FLOOR ONE S9 Artist Steam was designed with those spaces in mind. Its lay-flat design allows it to clean beneath couches, coffee tables, beds and other low-clearance furniture without requiring homeowners to pull everything back out again. Combined with steam cleaning, fresh-water washing and debris pickup, it helps tackle the dirt, debris and residue that can be left behind after a busy evening of entertaining.
For many hosts, post-party cleanup isn't just about wiping counters or loading the dishwasher. It's also about dealing with the messes that become visible when a room is temporarily rearranged, but are easy to overlook once everything is back in place. Because after guests leave, putting the furniture back is often the easy part. Cleaning what was hiding underneath it can be another story.
The Tineco FLOOR ONE S9 Artist Steam is available through Tineco U.S. and select retail partners.
About Tineco
Tineco ("tin-co") was founded in 1998 with its first product launch as a vacuum cleaner and, in 2019, pioneered the first-ever smart vacuum. Today, the brand has evolved into a global leader in intelligent appliances spanning floor care, kitchen, and personal care categories. With a growing user base of over 24 million households and availability in approximately 30 countries worldwide, Tineco remains committed to its brand vision of making life easier through smart technology and continuous innovation.
For more information, visit Tineco U.S.
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The Cleanest Looking Room in the House, May Be Hiding the Most Dirt
LOS ANGELES, June 30, 2026 /PRNewswire/ -- Zenerate, an AI-powered real estate feasibility platform, is pleased to announce a new partnership with AvalonBay Communities, Inc., one of the leading multifamily housing companies in the United States.
AvalonBay is focused on developing, redeveloping, acquiring, and managing distinctive apartment communities in select U.S. markets. Through this partnership, AvalonBay will use Zenerate's enterprise software platform to support early-stage development feasibility workflows.
Zenerate's platform helps development teams evaluate site potential, generate and compare design scenarios, review unit mix and parking assumptions, and analyze project feasibility during the earliest stages of decision-making. For multifamily developers, these early evaluations are critical to understanding whether a site aligns with project goals, development constraints, and investment strategy before moving deeper into acquisition, design, financing, or entitlement.
The partnership reflects the growing role of technology in real estate development. As development teams navigate changing market conditions, zoning requirements, construction costs, and site constraints, faster and more structured feasibility analysis can help reduce uncertainty and support better decision-making from the start.
By using Zenerate, AvalonBay teams can explore multiple development scenarios more efficiently and gain clearer insight into how different site planning assumptions may affect project outcomes. The platform is designed to help teams move from initial site review to actionable feasibility analysis with greater speed, clarity, and consistency.
"We are experiencing a powerful push for AI Transformation firsthand from global real estate development and management firms," said Benji Shin, CEO of Zenerate. "In fact, numerous global enterprises are already collaborating with us through the Zenerate platform and our custom software development services. This demand has grown exponentially this year compared to last."
This partnership represents another step in Zenerate's continued growth as a feasibility platform for multifamily developers and real estate development teams. By combining AI-powered design automation with real-time feasibility analysis, Zenerate helps teams evaluate more opportunities, compare development options, and make more informed early-stage decisions.
About Zenerate
Zenerate is an AI-powered real estate feasibility platform that helps developers evaluate development opportunities faster. The platform supports early-stage feasibility workflows, including site planning, test fits, unit mix studies, parking layouts, design option generation, and pro forma analysis. Zenerate helps teams explore multiple development scenarios quickly so they can make more informed decisions during acquisition, planning, and early design.
For more information, visit https://www.zenerate.ai.
About AvalonBay Communities
AvalonBay Communities, Inc. is a leading multifamily housing company focused on developing, redeveloping, acquiring, and managing distinctive apartment communities in select U.S. markets.
For more information, visit https://www.avaloncommunities.com.
** This press release is distributed by PR Newswire through automated distribution system, for which the client assumes full responsibility. **
Zenerate Announces Partnership with AvalonBay Communities to Support Early-Stage Multifamily Feasibility Analysis
